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» Feature Generation Using General Constructor Functions
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WSDM
2012
ACM
285views Data Mining» more  WSDM 2012»
12 years 3 months ago
Probabilistic models for personalizing web search
We present a new approach for personalizing Web search results to a specific user. Ranking functions for Web search engines are typically trained by machine learning algorithms u...
David Sontag, Kevyn Collins-Thompson, Paul N. Benn...
POPL
2005
ACM
14 years 8 months ago
Mutatis mutandis: safe and predictable dynamic software updating
Dynamic software updates can be used to fix bugs or add features to a running program without downtime. Essential for some applications and convenient for others, low-level dynami...
Gareth Stoyle, Michael W. Hicks, Gavin M. Bierman,...
WWW
2004
ACM
14 years 8 months ago
Is question answering an acquired skill?
We present a question answering (QA) system which learns how to detect and rank answer passages by analyzing questions and their answers (QA pairs) provided as training data. We b...
Ganesh Ramakrishnan, Soumen Chakrabarti, Deepa Par...
KDD
2008
ACM
147views Data Mining» more  KDD 2008»
14 years 8 months ago
Structured learning for non-smooth ranking losses
Learning to rank from relevance judgment is an active research area. Itemwise score regression, pairwise preference satisfaction, and listwise structured learning are the major te...
Soumen Chakrabarti, Rajiv Khanna, Uma Sawant, Chir...
KDD
2008
ACM
119views Data Mining» more  KDD 2008»
14 years 8 months ago
SAIL: summation-based incremental learning for information-theoretic clustering
Information-theoretic clustering aims to exploit information theoretic measures as the clustering criteria. A common practice on this topic is so-called INFO-K-means, which perfor...
Junjie Wu, Hui Xiong, Jian Chen